Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

Reservoir Operation Optimization: A Nonstructural Solution for Control of Seepage from Lar Reservoir in Iran

Abstract

In this paper, the results of utilizing a deterministic dynamic programming model for operation of Lar Reservoir in Iran are discussed. This reservoir has experienced extensive seepage from the start of its operation. The optimization model consists of a three-step cycle, which began with the optimization of reservoir operation for a given set of streamflows. The optimal policies are then analyzed in a regression procedure to obtain a set of operating rules. After the first run, operating rules from the previous run were placed as a new constraint on the water releases with some pre-assigned tolerance and the cycle continues. The model also consisted of mathematical functions for modeling the seepage from Lar Reservoir as a function of storage head in the reservoir. The loss function in the model was also modified in order to incorporate parameters that reduce the seepage. Results of different scenarios showed the significant effect of optimal policies on reduction of seepage and increasing the reliability of water supply to Tehran Metropolitan Area. A pumping station was also proposed to utilize the inactive part of the reservoir, in access of over 100 MCM, in order to reduce the seepage. The effectiveness of different pumping capacities to reduce the seepage was also investigated.     

High-Frequency Shear Modulus and Relaxation Time of Soft-Sphere and Lennard–Jones Fluids

The high-frequency shear modulus, G, and shear relaxation time, _shear, are obtained using the Zwanzig–Mountain equation for soft-sphere and Lennard-Jones potentials. The Hansen and Weis soft-sphere radial distribution function and the Matteoli–Mansoori Lennard-Jones radial distribution function are used in the equation. The shear relaxation times of different isotherms for both of these fluids pass through a minimum at a reduced density of about 0.7, which indicates a change from fluid-like behavior to viscoelastic behavior. The origins of this common density point are discussed. It is also shown that for the Lennard-Jones fluid, if the ratio of the reduced relaxation time to a power of the reduced temperature is plotted as a function of the reduced density, all isotherms become superimposed on a single curve.     

Highly efficient dye-sensitized solar cell prepared by electrophoretic deposition method: the effect of TiO<Subscript>2</Subscript> films thickness on the performance of cells

In this work, nanocrystalline P25 TiO₂ films with different thicknesses were deposited on FTO coated glass substrates by an electrophoretic deposition technique (EPD) and applied as the work electrode for dye-sensitized solar cells (DSSC) using cis-bis(isothiocyanato)(2,2'-bipyridyl-4,4'-dicarboxylato)(4,4'di-nonyl-2'-bipyridyl) ruthenium(II) (Z907, Dyesol) as sensitizing dye.The results showed that the increasing the thickness of TiO₂ films lead to increase the adsorption of the dye on the TiO₂ layers which in turns improved the short-circuit photocurrent (J_sc) and open-circuit voltage (V_oc), respectively. Furthermore, it was found that the effects of the surface states on the recombination of the photo-injected electrons (electron–hole pairs) in the TiO₂ films strongly depend on theTiO₂ electrode annealing temperature. Finally, a DSSC with a 32.82 μm thickness for TiO₂ film annealed at 600°C produced the highest conversion efficiency with an incident solar energy of 100 mW/cm² (η = 8.23%, J_sc = 15.98, V_oc = 0.73, FF = 0.7).     

A statistical model for extracting geometric sources of transistor performance variation

This paper provides an approach to extracting geometrical variations in nominally identical devices fabricated in close proximity to each other. The method assumes correlations between factors can be neglected. With this assumption, it is shown that geometrical variations are significant, and any study of microscopic variations, such as dopant fluctuations, must first strip away these macroscopic geometrical variations. We assess the gate length L and width W dependence of threshold voltage (V/sub T/) variations. Related geometrical variations, namely corner influences where the source and drain encounter the isolation edges are examined, and incorporated in the model. Results from the model are compared to measurements at small dimensions. The differences provide lower bounds for excess variations other than these geometrical contributions. Our study shows that these other variations account for almost half the total V/sub T/ variance at the smallest device size fabricated, demonstrating the seriousness of these other variations when scaling down devices.     

Analysis of dual-VT SRAM cells with full-swingsingle-ended bit line sensing for on-chip cache

This paper compares different high-V_T and dual-V_T design choices for a large on-chip cache with single-ended sensing in a 0.13 μm technology generation. The analysis shows that the best design is the one using a dual-V_T cell, with minimum channel length pass transistors, and low-V_T peripheral circuits. This dual-V_T circuit provides 20% performance gain with only 1.3× larger active leakage power, and 2.4% larger cell area compared to the best design using high-V_T cells with nonminimum channel length pass transistors     

Spatial Scale Resolution of Prognostic Hydrological Models: Simulation Performance and Application in Climate Change Impact Assessment

Water Resources Management - In this paper, long-term hydrological response of a watershed to climate change was investigated taking into account the spatial scale effect on the performance of...     

Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks

Data mining and approaches based on it have always been of approaches that have been considered in solving problems in the field of computer, but on some issues, this approach has been neglected. The area of wireless sensor networks and specifically the issue of optimal determining of the cluster head node are of these issues. To solve the problem of optimal determining of the cluster head node, Naïve Bayes that is the subset of data mining techniques is used in this paper. The results obtained after simulation of the presented algorithm show that the efficiency of this algorithm is significantly higher compared with other approaches that have so far been used to solve this problem, and thus it can be said that using this algorithm will lead to improved outcomes of solving this problem.     

Radiation dose to neonates undergoing X-ray imaging in special care baby units in Iran

Radiographic imaging has a significant role in the timely diagnosis of the diseases of neonates in intensive care units. The estimation of the dose received by the infants undergoing radiographic examination is of great importance, due to greater more radiosensitivity and longer life expectancy of the neonates and premature babies. In this study, the values of entrance skin dose (ESD), dose area products (DAPs), energy imparted (EI), whole-body dose, effective dose and risk of childhood cancer were estimated using three methods including direct method [using thermoluminescence dosimetry (TLD) chips], indirect method (using tube output) and Monte Carlo (MC) method (using MCNP4C code). In the first step, the ESD of the neonates was directly measured using TLD-100 chips. Fifty neonates, mostly premature, with different weights and gestational ages in five hospitals mostly suffering from respiratory distress syndrome and pneumonia were involved in this study. In the second step, the values of ESD to neonates were indirectly obtained from the tube output in different imaging techniques. The imaging room, incubator, neonates and other components were then simulated in order to obtain the ESD values using the MCNP4C code. Finally, the values of ESD assessed by the three methods were used for calculation of DAP, EI, whole-body dose, effective dose and risk of childhood cancer. The results indicate that the mean ESD per radiograph estimated by the direct, indirect and MC methods are 56.6±4.1, 50.1±3.1 and 54.5±3.3 μGy, respectively. The mean risk of childhood cancer estimated in this study varied between 4.21×10(-7) and 2.72×10(-6).